






"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
snake_case : List[Any]     		=		logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt""")



@dataclass
class        UpperCamelCase__ :







    """simple docstring"""
    __UpperCAmelCase           = field(
        default="""cifar10"""							,	metadata={"""help""": """Name of a dataset from the datasets package"""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """The column name of the images in the files."""})
    __UpperCAmelCase           = field(default=a_							,	metadata={"""help""": """A folder containing the training data."""})
    __UpperCAmelCase           = field(default=a_							,	metadata={"""help""": """A folder containing the validation data."""})
    __UpperCAmelCase           = field(
        default=0.15							,	metadata={"""help""": """Percent to split off of train for validation."""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of training examples to this """
                """value if set."""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                """value if set."""
            )
        }							,	)







    def 				a__			(		self :	Tuple ):





           '''simple docstring'''

           __magic_name__      				=    {}
           if self.train_dir is not None:
                  __magic_name__      				=    self.train_dir
           if self.validation_dir is not None:
                  __magic_name__      				=    self.validation_dir
           __magic_name__      				=    data_files if data_files else None



@dataclass
class        UpperCamelCase__ :







    """simple docstring"""
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """Override some existing default config settings when a model is trained from scratch. Example: """
                """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""})
    __UpperCAmelCase           = field(
        default="""main"""							,	metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}							,	)
    __UpperCAmelCase           = field(default=a_							,	metadata={"""help""": """Name or path of preprocessor config."""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                """with private models)."""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=0.75							,	metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Whether or not to train with normalized pixel values as target."""})



@dataclass
class        UpperCamelCase__ (				a_):







    """simple docstring"""
    __UpperCAmelCase           = field(
        default=1E-3							,	metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""})


def 							A						(				__snake_case: Optional[int]  )    ->		Tuple:

       """simple docstring"""





       __magic_name__      				=    torch.stack([example['pixel_values'] for example in examples]  )
       return {"pixel_values": pixel_values}


def 							A						(				)    ->		List[str]:

       """simple docstring"""





       __magic_name__      				=    HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)  )
       if len(sys.argv  ) == 2 and sys.argv[1].endswith('.json'  ):
              # If we pass only one argument to the script and it's the path to a json file,
              # let's parse it to get our arguments.
              __magic_name__				,  __magic_name__				,  __magic_name__      				=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
       else:
              __magic_name__				,  __magic_name__				,  __magic_name__      				=    parser.parse_args_into_dataclasses()

       # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
       # information sent is the one passed as arguments along with your Python/PyTorch versions.
       send_example_telemetry('run_mae' ,     __snake_case ,     __snake_case  )

       # Setup logging
       logging.basicConfig(
           format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' ,     datefmt='%m/%d/%Y %H:%M:%S' ,     handlers=[logging.StreamHandler(sys.stdout  )] ,     )

       if training_args.should_log:
              # The default of training_args.log_level is passive, so we set log level at info here to have that default.
              transformers.utils.logging.set_verbosity_info()

       __magic_name__      				=    training_args.get_process_log_level()
       logger.setLevel(__snake_case  )
       transformers.utils.logging.set_verbosity(__snake_case  )
       transformers.utils.logging.enable_default_handler()
       transformers.utils.logging.enable_explicit_format()

       # Log on each process the small summary:
       logger.warning(
           F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
           + F"""distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}"""  )
       logger.info(F"""Training/evaluation parameters {training_args}"""  )

       # Detecting last checkpoint.
       __magic_name__      				=    None
       if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
              __magic_name__      				=    get_last_checkpoint(training_args.output_dir  )
              if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
                     raise ValueError(
                         F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
                         'Use --overwrite_output_dir to overcome.'  )
              elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
                     logger.info(
                         F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
                         'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'  )

    # Initialize our dataset.
       __magic_name__      				=    load_dataset(
           data_args.dataset_name ,     data_args.dataset_config_name ,     data_files=data_args.data_files ,     cache_dir=model_args.cache_dir ,     use_auth_token=True if model_args.use_auth_token else None ,     )

       # If we don't have a validation split, split off a percentage of train as validation.
       __magic_name__      				=    None if 'validation' in ds.keys() else data_args.train_val_split
       if isinstance(data_args.train_val_split ,     __snake_case  ) and data_args.train_val_split > 0.0:
              __magic_name__      				=    ds['train'].train_test_split(data_args.train_val_split  )
              __magic_name__      				=    split['train']
              __magic_name__      				=    split['test']

       # Load pretrained model and image processor
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.
       __magic_name__      				=    {
           'cache_dir': model_args.cache_dir,
           'revision': model_args.model_revision,
           'use_auth_token': True if model_args.use_auth_token else None,
       }
       if model_args.config_name:
              __magic_name__      				=    ViTMAEConfig.from_pretrained(model_args.config_name ,     **__snake_case  )
       elif model_args.model_name_or_path:
              __magic_name__      				=    ViTMAEConfig.from_pretrained(model_args.model_name_or_path ,     **__snake_case  )
       else:
              __magic_name__      				=    ViTMAEConfig()
              logger.warning('You are instantiating a new config instance from scratch.'  )
              if model_args.config_overrides is not None:
                     logger.info(F"""Overriding config: {model_args.config_overrides}"""  )
                     config.update_from_string(model_args.config_overrides  )
                     logger.info(F"""New config: {config}"""  )

    # adapt config
       config.update(
           {
               'mask_ratio': model_args.mask_ratio,
               'norm_pix_loss': model_args.norm_pix_loss,
           }  )

       # create image processor
       if model_args.image_processor_name:
              __magic_name__      				=    ViTImageProcessor.from_pretrained(model_args.image_processor_name ,     **__snake_case  )
       elif model_args.model_name_or_path:
              __magic_name__      				=    ViTImageProcessor.from_pretrained(model_args.model_name_or_path ,     **__snake_case  )
       else:
              __magic_name__      				=    ViTImageProcessor()

       # create model
       if model_args.model_name_or_path:
              __magic_name__      				=    ViTMAEForPreTraining.from_pretrained(
                  model_args.model_name_or_path ,     from_tf=bool('.ckpt' in model_args.model_name_or_path  ) ,     config=__snake_case ,     cache_dir=model_args.cache_dir ,     revision=model_args.model_revision ,     use_auth_token=True if model_args.use_auth_token else None ,     )
       else:
              logger.info('Training new model from scratch'  )
              __magic_name__      				=    ViTMAEForPreTraining(__snake_case  )

       if training_args.do_train:
              __magic_name__      				=    ds['train'].column_names
       else:
              __magic_name__      				=    ds['validation'].column_names

       if data_args.image_column_name is not None:
              __magic_name__      				=    data_args.image_column_name
       elif "image" in column_names:
              __magic_name__      				=    'image'
       elif "img" in column_names:
              __magic_name__      				=    'img'
       else:
              __magic_name__      				=    column_names[0]

       # transformations as done in original MAE paper
       # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
       if "shortest_edge" in image_processor.size:
              __magic_name__      				=    image_processor.size['shortest_edge']
       else:
              __magic_name__      				=    (image_processor.size['height'], image_processor.size['width'])
       __magic_name__      				=    Compose(
           [
               Lambda(lambda __snake_case  : img.convert('RGB'  ) if img.mode != "RGB" else img  ),
               RandomResizedCrop(__snake_case ,     scale=(0.2, 1.0) ,     interpolation=InterpolationMode.BICUBIC  ),
               RandomHorizontalFlip(),
               ToTensor(),
               Normalize(mean=image_processor.image_mean ,     std=image_processor.image_std  ),
           ]  )

       def preprocess_images(__snake_case: Tuple  ):
              __magic_name__      				=    [transforms(__snake_case  ) for image in examples[image_column_name]]
              return examples

       if training_args.do_train:
              if "train" not in ds:
                     raise ValueError('--do_train requires a train dataset'  )
              if data_args.max_train_samples is not None:
                     __magic_name__      				=    ds['train'].shuffle(seed=training_args.seed  ).select(range(data_args.max_train_samples  )  )
              # Set the training transforms
              ds["train"].set_transform(__snake_case  )

       if training_args.do_eval:
              if "validation" not in ds:
                     raise ValueError('--do_eval requires a validation dataset'  )
              if data_args.max_eval_samples is not None:
                     __magic_name__      				=    (
                         ds['validation'].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
                     )
              # Set the validation transforms
              ds["validation"].set_transform(__snake_case  )

       # Compute absolute learning rate
       __magic_name__      				=    (
           training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
       )
       if training_args.base_learning_rate is not None:
              __magic_name__      				=    training_args.base_learning_rate * total_train_batch_size / 2_5_6

       # Initialize our trainer
       __magic_name__      				=    Trainer(
           model=__snake_case ,     args=__snake_case ,     train_dataset=ds['train'] if training_args.do_train else None ,     eval_dataset=ds['validation'] if training_args.do_eval else None ,     tokenizer=__snake_case ,     data_collator=__snake_case ,     )

       # Training
       if training_args.do_train:
              __magic_name__      				=    None
              if training_args.resume_from_checkpoint is not None:
                     __magic_name__      				=    training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     __magic_name__      				=    last_checkpoint
              __magic_name__      				=    trainer.train(resume_from_checkpoint=__snake_case  )
              trainer.save_model()
              trainer.log_metrics('train' ,     train_result.metrics  )
              trainer.save_metrics('train' ,     train_result.metrics  )
              trainer.save_state()

       # Evaluation
       if training_args.do_eval:
              __magic_name__      				=    trainer.evaluate()
              trainer.log_metrics('eval' ,     __snake_case  )
              trainer.save_metrics('eval' ,     __snake_case  )

       # Write model card and (optionally) push to hub
       __magic_name__      				=    {
           'tasks': 'masked-auto-encoding',
           'dataset': data_args.dataset_name,
           'tags': ['masked-auto-encoding'],
       }
       if training_args.push_to_hub:
              trainer.push_to_hub(**__snake_case  )
       else:
              trainer.create_model_card(**__snake_case  )


def 							A						(				__snake_case: int  )    ->		Optional[Any]:

       """simple docstring"""





       main()


if __name__ == "__main__":
 main()







"""simple docstring"""
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


snake_case : List[Any]     		=		{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case : List[Any]     		=		{
    """vocab_file""": {
        """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """unc-nlp/lxmert-base-uncased""": (
            """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case : Optional[int]     		=		{
    """unc-nlp/lxmert-base-uncased""": 5_1_2,
}

snake_case : List[Any]     		=		{
    """unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}



class        UpperCamelCase__ (				a_):







    """simple docstring"""
    __UpperCAmelCase           = VOCAB_FILES_NAMES
    __UpperCAmelCase           = PRETRAINED_VOCAB_FILES_MAP
    __UpperCAmelCase           = PRETRAINED_INIT_CONFIGURATION
    __UpperCAmelCase           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCAmelCase           = LxmertTokenizer







    def __init__(		self :	Union[str, Any]				,    UpperCamelCase_ :	List[str]=None				,    UpperCamelCase_ :	Dict=None				,    UpperCamelCase_ :	Optional[int]=True				,    UpperCamelCase_ :	Dict="[UNK]"				,    UpperCamelCase_ :	str="[SEP]"				,    UpperCamelCase_ :	Dict="[PAD]"				,    UpperCamelCase_ :	Optional[int]="[CLS]"				,    UpperCamelCase_ :	Any="[MASK]"				,    UpperCamelCase_ :	Any=True				,    UpperCamelCase_ :	List[Any]=None				,    **UpperCamelCase_ :	Dict				,    ):





           '''simple docstring'''

           super().__init__(
               UpperCamelCase_				,    tokenizer_file=UpperCamelCase_				,    do_lower_case=UpperCamelCase_				,    unk_token=UpperCamelCase_				,    sep_token=UpperCamelCase_				,    pad_token=UpperCamelCase_				,    cls_token=UpperCamelCase_				,    mask_token=UpperCamelCase_				,    tokenize_chinese_chars=UpperCamelCase_				,    strip_accents=UpperCamelCase_				,    **UpperCamelCase_				,    )

           __magic_name__      				=    json.loads(self.backend_tokenizer.normalizer.__getstate__() )
           if (
               normalizer_state.get('lowercase'				,    UpperCamelCase_ ) != do_lower_case
               or normalizer_state.get('strip_accents'				,    UpperCamelCase_ ) != strip_accents
               or normalizer_state.get('handle_chinese_chars'				,    UpperCamelCase_ ) != tokenize_chinese_chars
           ):
                  __magic_name__      				=    getattr(UpperCamelCase_				,    normalizer_state.pop('type' ) )
                  __magic_name__      				=    do_lower_case
                  __magic_name__      				=    strip_accents
                  __magic_name__      				=    tokenize_chinese_chars
                  __magic_name__      				=    normalizer_class(**UpperCamelCase_ )

           __magic_name__      				=    do_lower_case







    def 				a__			(		self :	Dict				,    UpperCamelCase_ :	Optional[Any]				,    UpperCamelCase_ :	Optional[int]=None ):





           '''simple docstring'''

           __magic_name__      				=    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

           if token_ids_a:
                  output += token_ids_a + [self.sep_token_id]

           return output







    def 				a__			(		self :	int				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None ):





           '''simple docstring'''

           __magic_name__      				=    [self.sep_token_id]
           __magic_name__      				=    [self.cls_token_id]
           if token_ids_a is None:
                  return len(cls + token_ids_a + sep ) * [0]
           return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







    def 				a__			(		self :	Dict				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Optional[str] = None ):





           '''simple docstring'''

           __magic_name__      				=    self._tokenizer.model.save(UpperCamelCase_				,    name=UpperCamelCase_ )
           return tuple(UpperCamelCase_ )